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\title{
\begin{center}
    {\Large Project Work\\}                  
    {\LARGE\bfseries Accuracy of Machine Learning Based Wi-Fi Localization
\\}     
\end{center}
}
\author{Zijian Zhou\\Information and Media Technology\\\\\\\\at Institute of Telematics\\
Prof.Volker Turau\\
(Supervisor: Julian Ohrt)}
\date{September 27, 2013}
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%Indoor localization is a blind zone for navigation system base on GPS since GPS signal is not accessible inside a building. This project work implements a indoor localization solution based on Wi-Fi fingerprints and machine learning classification. This simple approaching requires no additional hardware devices. Under the fact that walls attenuate Wi-Fi signals significantly, the fingerprint of Wi-Fi signals in rooms follow distinguish pattern, hence indoor localization with precision in room level can be achieved. This report compares several machine learning algorithms in classifying Wi-Fi patterns in rooms and gives a demonstrative application in Android to show how this approach is implemented. 
I hereby certify that this thesis has been composed by me and is based on my own work, unless stated otherwise. No other person's work has been used without due acknowledgement in this thesis. All references and verbatim extracts have been quoted, and all sources of information,including graphs and data sets, have been specifically acknowledged.\\\\\\\\
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